




"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase				:     int					=				logging.get_logger(__name__)

lowerCAmelCase				:     Tuple					=				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase				:     Optional[Any]					=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   List[Any]:
			for attribute in key.split("""."""							):
						lowerCamelCase      =   getattr(snake_case__			,					snake_case__							)

			if weight_type is not None:
						lowerCamelCase      =   getattr(snake_case__			,					snake_case__							).shape
			else:
						lowerCamelCase      =   hf_pointer.shape

			assert hf_shape == value.shape, (
			    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
			    F' {value.shape} for {full_name}'
			)

			if weight_type == "weight":
						lowerCamelCase      =   value
			elif weight_type == "weight_g":
						lowerCamelCase      =   value
			elif weight_type == "weight_v":
						lowerCamelCase      =   value
			elif weight_type == "bias":
						lowerCamelCase      =   value
			else:
						lowerCamelCase      =   value

			logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)



def        a__					(							snake_case__			,					snake_case__							)		->   str:
			lowerCamelCase      =   []
			lowerCamelCase      =   fairseq_model.state_dict()

			lowerCamelCase      =   hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase      =   None

			for name, value in fairseq_dict.items():
						lowerCamelCase      =   False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					hf_model.config.feat_extract_norm == """group"""			,					)
									lowerCamelCase      =   True
						elif name.split("""."""							)[0] == "proj":
									lowerCamelCase      =   fairseq_model.proj
									lowerCamelCase      =   True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
															lowerCamelCase      =   True
															if "*" in mapped_key:
																		lowerCamelCase      =   name.split(snake_case__							)[0].split("""."""							)[-2]
																		lowerCamelCase      =   mapped_key.replace("""*"""			,					snake_case__							)
															if "weight_g" in name:
																		lowerCamelCase      =   """weight_g"""
															elif "weight_v" in name:
																		lowerCamelCase      =   """weight_v"""
															elif "bias" in name:
																		lowerCamelCase      =   """bias"""
															elif "weight" in name:
																		lowerCamelCase      =   """weight"""
															else:
																		lowerCamelCase      =   None
															set_recursively(snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)
												continue
						if not is_used:
									unused_weights.append(snake_case__							)

			logger.warning(F'Unused weights: {unused_weights}'							)

			return proj_weight



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   int:
			lowerCamelCase      =   full_name.split("""conv_layers."""							)[-1]
			lowerCamelCase      =   name.split("""."""							)
			lowerCamelCase      =   int(items[0]							)
			lowerCamelCase      =   int(items[1]							)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
									    " found."
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
			else:
						unused_weights.append(snake_case__							)



def        a__					(							snake_case__							)		->   Optional[Any]:
			lowerCamelCase ,  lowerCamelCase      =   emb.weight.shape
			lowerCamelCase      =   nn.Linear(snake_case__			,					snake_case__			,					bias=snake_case__							)
			lowerCamelCase      =   emb.weight.data
			return lin_layer



def        a__					(							snake_case__							)		->   List[Any]:
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as f:
						lowerCamelCase      =   f.readlines()
						lowerCamelCase      =   [line.split(""" """							)[0] for line in lines]

			lowerCamelCase      =   len(snake_case__							)

			lowerCamelCase      =   {
			    """<s>""": 0,
			    """<pad>""": 1,
			    """</s>""": 2,
			    """<unk>""": 3,
			}

			vocab_dict.update(dict(zip(snake_case__			,					range(4			,					num_words + 4							)							)							)							)
			return vocab_dict



@torch.no_grad()
def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					)		->   List[str]:
			lowerCamelCase      =   WavaVecaConfig.from_pretrained(snake_case__							)
			lowerCamelCase      =   SpeechaTextaConfig.from_pretrained(
			    snake_case__			,					vocab_size=snake_case__			,					decoder_layers=snake_case__			,					do_stable_layer_norm=snake_case__							)

			lowerCamelCase      =   WavaVecaFeatureExtractor(
			    feature_size=1			,					sampling_rate=1_60_00			,					padding_value=0			,					do_normalize=snake_case__			,					return_attention_mask=snake_case__			,					)

			lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]			,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
			lowerCamelCase      =   model[0].eval()

			# set weights for wav2vec2 encoder
			lowerCamelCase      =   WavaVecaModel(snake_case__							)
			lowerCamelCase      =   recursively_load_weights_wavaveca(model.encoder			,					snake_case__							)

			lowerCamelCase      =   SpeechaTextaForCausalLM(snake_case__							)
			lowerCamelCase ,  lowerCamelCase      =   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,					strict=snake_case__							)

			# set output linear layer
			unexpected_keys.remove("""embed_out"""							)
			lowerCamelCase      =   nn.Parameter(model.decoder.embed_out.detach()							)

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'							)
			logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'							)

			lowerCamelCase      =   SpeechEncoderDecoderModel(encoder=snake_case__			,					decoder=snake_case__							)
			lowerCamelCase      =   False

			# add projection layer
			lowerCamelCase      =   nn.Parameter(projection_layer.weight							)
			lowerCamelCase      =   nn.Parameter(projection_layer.bias							)

			lowerCamelCase      =   create_vocab_dict(snake_case__							)

			with open(os.path.join(snake_case__			,					"""vocab.json"""							)			,					"""w"""							) as fp:
						json.dump(snake_case__			,					snake_case__							)

			lowerCamelCase      =   SpeechaTextaTokenizer(os.path.join(snake_case__			,					"""vocab.json"""							)							)
			tokenizer.save_pretrained(snake_case__							)

			lowerCamelCase      =   hf_wavavec.config.to_dict()
			lowerCamelCase      =   tokenizer.pad_token_id
			lowerCamelCase      =   tokenizer.bos_token_id
			lowerCamelCase      =   tokenizer.eos_token_id
			lowerCamelCase      =   """speech_to_text_2"""
			lowerCamelCase      =   """wav2vec2"""

			lowerCamelCase      =   SpeechEncoderDecoderConfig.from_dict(snake_case__							)

			hf_wavavec.save_pretrained(snake_case__							)
			feature_extractor.save_pretrained(snake_case__							)


if __name__ == "__main__":
					lowerCAmelCase				:     Any					=				argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument(
					    """--encoder_config_path""",
					    default="""facebook/wav2vec2-large-lv60""",
					    type=str,
					    help="""Path to hf encoder wav2vec2 checkpoint config""",
					)
					parser.add_argument(
					    """--decoder_config_path""",
					    default="""facebook/s2t-small-mustc-en-fr-st""",
					    type=str,
					    help="""Path to hf decoder s2t checkpoint config""",
					)
					parser.add_argument("""--vocab_size""", default=1_0224, type=int, help="""Vocab size of decoder""")
					parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

					lowerCAmelCase				:     List[str]					=				parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    vocab_size=args.vocab_size,
					    num_decoder_layers=args.num_decoder_layers,
					)









"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



